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CsPbBr3 quantum dots/PDVT-10 conjugated polymer hybrid film-based photonic synaptic transistors toward high-efficiency neuromorphic computing

基于CsPbBr3量子点/PDVT-10共轭聚合物杂化薄膜 的光突触晶体管用于高效的神经形态计算

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Abstract

Photonic synaptic transistors are promising neuromorphic computing systems that are expected to circumvent the intrinsic limitations of von Neumann-based computation. The design and construction of photonic synaptic transistors with a facile fabrication process and high-efficiency information processing ability are highly desired, while it remains a tremendous challenge. Herein, a new approach based on spin coating of a blend of CsPbBr3 perovskite quantum dot (QD) and PDVT-10 conjugated polymer is reported for the fabrication of photonic synaptic transistors. The combination of flat surface, outstanding optical absorption, and remarkable charge transporting performance contributes to high-efficiency photon-to-electron conversion for such perovskite-based synapses. High-performance photonic synaptic transistors are thus fabricated with essential synaptic functionalities, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and long-term memory. By utilizing the photonic potentiation and electrical depression features, perovskite-based photonic synaptic transistors are also explored for neuromorphic computing simulations, showing high pattern recognition accuracy of up to 89.98%, which is one of the best values reported so far for synaptic transistors used in pattern recognition. This work provides an effective and convenient pathway for fabricating perovskite-based neuromorphic systems with high pattern recognition accuracy.

摘要

光突触晶体管被视为有潜力的神经形态计算系统, 有望克服基 于冯诺依曼架构运算的固有限制. 然而, 具备简单制备工艺和高效信息 处理能力的光突触晶体管的设计和构建面临着巨大的挑战. 本文报道 了一种通过旋涂CsPbBr3钙钛矿量子点(QDs)和PDVT-10共轭聚合物共 混物来制备光突触晶体管的新方法. 由CsPbBr3 QDs和PDVT-10组成的 杂化薄膜具有平坦的表面、优异的光吸收和良好的电荷传输性能, 有 助于此类钙钛矿基突触实现高效的光电转换. 因此, 基于CsPbBr3 QDs 和PDVT-10杂化薄膜的光突触晶体管表现出了优异的器件性能, 并具 有基本的突触功能, 包括兴奋性突触后电流、双脉冲促进和长程记忆. 通过利用光增强和电抑制特性, 基于钙钛矿的光突触晶体管被成功应 用于神经形态计算, 其模式识别精度高达89.98%, 这是迄今为止用于模 式识别的突触晶体管的最高值之一. 这项工作为制备高模式识别精度 的钙钛矿基神经形态系统提供了一条有效且方便的途径.

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Acknowledgements

This work was supported by the Ministry of Science and Technology of the People’s Republic of China (2018YFA0703200), the National Natural Science Foundation of China (91833306, 51633006, 51703160, 51733004, 51725304, and 52003189), and Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China (2021ZZ130 and 2021ZZ129).

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Authors and Affiliations

  1. Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, China

    Congyong Wang  (王聪勇), Shuming Duan  (段树铭), Jishan Wu  (吴继善) & Wenping Hu  (胡文平)

  2. Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore

    Congyong Wang  (王聪勇) & Jishan Wu  (吴继善)

  3. Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University & Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China

    Qisheng Sun  (孙启升), Xiaotao Zhang  (张小涛), Shuming Duan  (段树铭) & Wenping Hu  (胡文平)

  4. Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China

    Gang Peng  (彭港), Xipeng Yu  (于希鹏), Enlong Li  (李恩龙), Rengjian Yu  (俞礽坚), Changsong Gao  (高昌松) & Huipeng Chen  (陈惠鹏)

  5. Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China

    Huipeng Chen  (陈惠鹏)

  6. School of Materials Science and Engineering, Xiamen University of Technology, Xiamen, 361024, China

    Yujie Yan  (严育杰)

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  1. Congyong Wang  (王聪勇)
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  2. Qisheng Sun  (孙启升)
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  3. Gang Peng  (彭港)
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  4. Yujie Yan  (严育杰)
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  5. Xipeng Yu  (于希鹏)
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  6. Enlong Li  (李恩龙)
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  7. Rengjian Yu  (俞礽坚)
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  9. Xiaotao Zhang  (张小涛)
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  10. Shuming Duan  (段树铭)
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  11. Huipeng Chen  (陈惠鹏)
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  13. Wenping Hu  (胡文平)
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Contributions

Hu W, Wu J, Chen H, Duan S and Wang C conceived and supervised the project; Hu W, Wu J, Chen H, Duan S and Wang C wrote the paper; Wang C, Sun Q, Peng G, Yu X, Li E and Yu R designed and performed the experiments. All authors contributed to the general discussion.

Corresponding authors

Correspondence to Shuming Duan  (段树铭), Huipeng Chen  (陈惠鹏), Jishan Wu  (吴继善) or Wenping Hu  (胡文平).

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Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary information

Supporting data are available in the online version of the paper.

Congyong Wang received his MSc degree from Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences in 2019. Now he is a PhD candidate at the Department of Chemistry, Faculty of Science, National University of Singapore (NUS). His research focuses on the design and synthesis of functional materials and their applications in electronics.

Shuming Duan is an associate researcher at the Joint School of NUS and Tianjin University, International Campus of Tianjin University. He received his PhD degree from Tianjin University in 2019. Then he joined the Joint School of NUS and Tianjin University as a postdoctoral fellow. His research interests focus on printed organic crystalline circuits.

Huipeng Chen got his PhD degree in physics from Tufts University in 2009. Before joining the College of Physics and Information Engineering, Fuzhou University in 2015, he worked as a postdoctoral fellow at Texas Tech University during 2009–2011 and the University of Tennessee and Oak Ridge National Laboratory from 2011 to 2014. His research interest focuses on semiconductor materials and devices, including thin film transistors, memories, sensors, neuromorphic electronic devices and systems.

Jishan Wu is a professor at the Chemistry Department, NUS. He was awarded his PhD degree in 2004 from the Max-Planck Institute for Polymer Research (Germany). He held a postdoctoral position at the University of California at Los Angeles from 2005 to 2007 and then joined the NUS in 2007. His main research interests include novel π-conjugated systems and supramolecular chemistry.

Wenping Hu is a professor at Tianjin University. He received his PhD degree from the Institute of Chemistry, Chinese Academy of Sciences (ICCAS) in 1999. He then joined Osaka University as a Research Fellow supported by the Japan Society for the Promotion of Sciences, followed by a period at Stuttgart University as an Alexander von Humboldt fellow. In 2003, he worked for Nippon Telephone and Telegraph, before returning to the ICCAS where he was promoted to a full professor. In 2013, he joined Tianjin University. His research focuses on molecular electronics.

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CsPbBr3 quantum dots/PDVT-10 conjugated polymer hybrid film-based photonic synaptic transistors toward high-efficiency neuromorphic computing

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Wang, C., Sun, Q., Peng, G. et al. CsPbBr3 quantum dots/PDVT-10 conjugated polymer hybrid film-based photonic synaptic transistors toward high-efficiency neuromorphic computing. Sci. China Mater. 65, 3077–3086 (2022). https://doi.org/10.1007/s40843-022-2200-0

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